Systems and methods for quality assurance of radiation therapy

ABSTRACT

Systems and methods for a pre-treatment quality assurance (QA) of a radiotherapy device may be provided. The method may include determining a measured dose image through an electronic portal dose imaging device (EPID). The method may include determining an energy fluence distribution map related to radiation beams predicted by a first portal dose prediction model. The method may include determining a predicted dose image based on the energy fluence distribution map and a simulated energy response curve related to the EPID. The method may further include determining differences between the measured and predicted dose images by comparing the dose distributions of the measured and predicted dose images.

CROSS-REFERENCE OF RELATED APPLICATIONS

This application claims priority of Chinese Patent Application No.201811634279.3, filed on Dec. 29, 2018, the contents of which are herebyincorporated by reference.

TECHNICAL FIELD

The disclosure generally relates to radiation therapy technique, andmore particularly relates to systems and methods for pre-treatmentquality assurance (QA) of a radiotherapy device.

BACKGROUND

In order to achieve precise radiation therapy and improve the efficiencyof tumor treatment, an image-guided radiation therapy (IGRT) apparatusis widely used in clinical applications. In the IGRT apparatus, anelectronic portal imaging device (EPID) is an imaging component foraccurately locating the tumor before or during the radiation treatment.According to an image (e.g., a portal image or a portal dose image)acquired by the EPID, a doctor can determine whether a subject'sposition is accurate, and whether the location or shape of the tumor haschanged, so as to reduce the possibility of irradiating normal tissuesand achieve precise radiation treatment.

In radiation therapy (hereinafter referred to as radiotherapy), atreatment plan system (TPS) can be used to predetermine a treatment planbefore the treatment commences. To achieve precise radiotherapy, theaccuracy in delivering a planned radiation dose to the subject based onthe predetermined treatment plan needs to be verified. Some qualityassurance (QA) tools and protocols are needed to verify that the plannedradiation dose is delivered to the subject. For example, conventionalfilms and/or dose map detectors are used to perform the pre-treatment QAverification. Compared with the conventional films and the dose mapdetectors, the use of the EPID may save the time of the pre-treatment QAverification and improve the verification efficiency. Therefore, it isdesirable to develop systems and methods for quality assurance ofradiation therapy using the EPID.

SUMMARY

In a first aspect of the present disclosure, a system is provided. Thesystem may include at least one storage device storing a set ofinstructions and at least one processor in communication with the atleast one storage device. When executing the set of instructions, the atleast one processor may direct the system to perform one or moreoperations as the following. The at least one processor may determine ameasured dose image through an electronic portal dose imaging device(EPID). The measured dose image may be indicative of a dose distributionof radiation beams measured by the EPID, and the measured radiationbeams may correspond to a planned radiation dose and a planned gantryangle. The at least one processor may determine an energy fluencedistribution map related to radiation beams predicted by a first portaldose prediction model. The predicted radiation beams may correspond tothe planned radiation dose and the planned gantry angle. The at leastone processor may determine a predicted dose image based on the energyfluence distribution map and a simulated energy response curve relatedto the EPID. The predicted dose image may be indicative of a dosedistribution of the predicted radiation beams. The at least oneprocessor may determine differences between the measured and predicteddose images by comparing the dose distributions of the measured andpredicted dose images.

In some embodiments, the at least one processor may obtain a pluralityof raw images with respect to the measured radiation beams through theEPID. The at least one processor may obtain one or more calibrationparameters. The at least one processor may calibrate, based on the oneor more calibration parameters, each of the plurality of raw images. Theat least one processor may form a final calibrated image based on theplurality of calibrated raw images. The at least one processor mayconvert the final calibrated image to the measured dose image.

In some embodiments, the at least one processor may obtain a pluralityof raw images with respect to the measured radiation beams through theEPID. The at least one processor may obtain one or more calibrationparameters. The at least one processor may summarize the plurality ofraw images. The at least one processor may form a final calibrated imageby calibrating, based on the one or more calibration parameters, thesummarized raw image. The at least one processor may convert the finalcalibrated image to the measured dose image.

In some embodiments, the one or more calibration parameters may includeat least one of a position offset value, a detector gain value, or acurve correction value.

In some embodiments, the at least one processor may determine theposition offset based on position deviations of first measuredflood-field images relative to a center of the EPID. The at least oneprocessor may determine the detector gain value based on a secondmeasured flood-field image and a beam profile value. The at least oneprocessor may determine the curve correction value based on a thirdmeasured flood-field image and a predicted flood-field image. The thirdmeasured flood-field image may be associated with the second measuredflood-field image, and the predicted flood-field image may be generatedusing a second portal dose prediction model.

In some embodiments, the first portal dose prediction model or thesecond portal dose prediction model may include a Monte Carlo (MC)simulation model.

In some embodiments, the at least one processor may correct a predictedoutput factor of the first portal dose prediction model based on anoutput correction factor. The at least one processor may determine theenergy fluence distribution map by feeding the corrected output factorto the first portal dose prediction model.

In some embodiments, the at least one processor may determine anintermediate predicted dose image based on the energy fluencedistribution map and the simulated energy response curve. The at leastone processor may determine the predicted dose image by correcting theintermediate predicted dose image using an absolute dose correctionfactor.

In some embodiments, the simulated energy response curve related to theEPID may be determined in advance by modeling an energy depositionefficiency of the EPID.

In a second aspect of the present disclosure, a method is provided. Themethod may include one or more operations. The one or more operationsmay be implemented on a computing device having at least one processorand at least one storage device. The at least one processor maydetermine a measured dose image through an electronic portal doseimaging device (EPID). The measured dose image may be indicative of adose distribution of radiation beams measured by the EPID, and themeasured radiation beams may correspond to a planned radiation dose anda planned gantry angle. The at least one processor may determine anenergy fluence distribution map related to radiation beams predicted bya first portal dose prediction model. The predicted radiation beams maycorrespond to the planned radiation dose and the planned gantry angle.The at least one processor may determine a predicted dose image based onthe energy fluence distribution map and a simulated energy responsecurve related to the EPID. The predicted dose image may be indicative ofa dose distribution of the predicted radiation beams. The at least oneprocessor may determine differences between the measured and predicteddose images by comparing the dose distributions of the measured andpredicted dose images.

In a third aspect of the present disclosure, a non-transitorycomputer-readable medium is provided. The non-transitorycomputer-readable medium includes at least one set of instructions. Whenthe at least one set of instructions are executed by at least oneprocessor of a computer device, the at least one set of instructionsdirects the at least one processor to perform one or more operations asthe following. The at least one processor may determine a measured doseimage through an electronic portal dose imaging device (EPID). Themeasured dose image may be indicative of a dose distribution ofradiation beams measured by the EPID, and the measured radiation beamsmay correspond to a planned radiation dose and a planned gantry angle.The at least one processor may determine an energy fluence distributionmap related to radiation beams predicted by a first portal doseprediction model. The predicted radiation beams may correspond to theplanned radiation dose and the planned gantry angle. The at least oneprocessor may determine a predicted dose image based on the energyfluence distribution map and a simulated energy response curve relatedto the EPID. The predicted dose image may be indicative of a dosedistribution of the predicted radiation beams. The at least oneprocessor may determine differences between the measured and predicteddose images by comparing the dose distributions of the measured andpredicted dose images.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities, andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. The drawings are not to scale. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIGS. 1 and 2 illustrate an exemplary medical system according to someembodiments of the present disclosure;

FIG. 3 is a schematic diagram illustrating an exemplary radiationtherapy apparatus according to some embodiments of the presentdisclosure;

FIG. 4 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary computing device according to someembodiments of the present disclosure;

FIG. 5 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary mobile device according to some embodimentsof the present disclosure;

FIG. 6 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure;

FIG. 7 is a flowchart illustrating an exemplary process for verifyingradiation dose according to some embodiments of the present disclosure;

FIG. 8A is a flowchart illustrating an exemplary process for determininga measured dose image according to some embodiments of the presentdisclosure;

FIG. 8B is a flowchart illustrating an exemplary process for determininga measured dose image according to some embodiments of the presentdisclosure;

FIG. 9 is a flowchart illustrating an exemplary process for determiningcalibration parameters according to some embodiments of the presentdisclosure;

FIG. 10 is a flowchart illustrating an exemplary process for determininga predicted dose image according to some embodiments of the presentdisclosure; and

FIG. 11 illustrates a simulated energy response curve related to an EPIDaccording to some embodiments of the present disclosure.

DETAILED DESCRIPTION

The following description is presented to enable any person skilled inthe art to make and use the present disclosure and is provided in thecontext of a particular application and its requirements. Variousmodifications to the disclosed embodiments will be readily apparent tothose skilled in the art, and the general principles defined herein maybe applied to other embodiments and applications without departing fromthe spirit and scope of the present disclosure. Thus, the presentdisclosure is not limited to the embodiments shown but is to be accordedthe widest scope consistent with the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including” when used in this disclosure, specify the presence of statedfeatures, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or other storage devices. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices may be provided on a computer-readable medium, such asa compact disc, a digital video disc, a flash drive, a magnetic disc, orany other tangible medium, or as a digital download (and can beoriginally stored in a compressed or installable format that needsinstallation, decompression, or decryption prior to execution). Suchsoftware code may be stored, partially or fully, on a storage device ofthe executing computing device, for execution by the computing device.Software instructions may be embedded in firmware, such as an erasableprogrammable read-only memory (EPROM). It will be further appreciatedthat hardware modules/units/blocks may be included in connected logiccomponents, such as gates and flip-flops, and/or can be included ofprogrammable units, such as programmable gate arrays or processors. Themodules/units/blocks or computing device functionality described hereinmay be implemented as software modules/units/blocks but may berepresented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, sections or assembly of differentlevels in ascending order. However, the terms may be displaced byanother expression if they achieve the same purpose.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

The flowcharts used in the present disclosure illustrate operations thatsystems implement according to some embodiments in the presentdisclosure. It is to be expressly understood, the operations of theflowchart may be implemented not in order. Conversely, the operationsmay be implemented in an inverted order, or simultaneously. Moreover,one or more other operations may be added to the flowcharts. One or moreoperations may be removed from the flowcharts.

Various embodiments of the systems and methods described in the presentdisclosure may be used for quality assurance (QA) of radiation treatmentand/or verifying a predetermined treatment plan. The system maydetermine dose differences between the planned radiation dosedistribution and the actual radiation dose distribution (i.e., theactual dose delivered to a subject (e.g., a patient) during theradiation treatment). In some embodiments, the system may determine thedose differences by comparing a predicted dose image and a measured doseimage. The predicted dose image may be generated by a portal doseprediction model (e.g., a Monte Carlo (MC) simulation model). The portaldose prediction model may simulate the radiation delivery between atreatment head and an EPID of an IGRT apparatus and generate thepredicted dose image. In some embodiments, the system may generate anenergy fluence distribution map related to radiation beams predicted (orsimulated) by the first portal dose prediction model. The system maydetermine the predicted dose image based on the energy fluencedistribution map and a simulated energy response curve related to theEPID. The predicted dose image may be indicative of a predicted dosedistribution of the predicted radiations. In some embodiments, thesystem may measure an actual dose image (i.e., the measured dose image)through the EPID. For example, in accordance with the same plannedradiation dose and gantry angle, the treatment head may deliver theradiations onto the EPID. The EPID may be configured to acquire imagedata to generate the measured dose image. The measured dose image may beindicative of a dose distribution of the measured radiations (i.e.,actual radiations). The system may compare the dose distributions of themeasured and the predicted dose images to evaluate radiation deliverydeviation or errors (e.g., dose differences between the predicted dosedistribution and the measured dose distribution). Then whether theplanned treatment plan is reasonable may be evaluated based on the dosedifferences.

With reference to the systems and methods described in the presentdisclosure, the predicted dose image may be determined based on theenergy fluence distribution map and the simulated energy response curve.The system may not only predict the dose distribution of the plannedradiation dose accurately, but also reduce or avoid complex particles(e.g., photons of the radiation beam) transport simulation ofconventional MC simulation algorithms. Therefore, the computationalefficiency of the system may be improved.

FIGS. 1 and 2 illustrate an exemplary medical system according to someembodiments of the present disclosure. As illustrated in FIG. 1 or FIG.2, the medical system 100 may include an image-guided radiation therapy(IGRT) apparatus 110, a network 120, one or more terminals 130, aprocessing device 140, and a storage device 150. The components in themedical system 100 may be connected in one or more of various ways.Merely by way of example, as illustrated in FIG. 1, the IGRT apparatus110 may be connected to the processing device 140 through the network120. As illustrated in FIG. 2, the IGRT apparatus 110 may be directlyconnected to the processing device 140 (as indicated by thebi-directional arrow in a solid line linking the IGRT apparatus 110 andthe processing device 140). As another example, the one or moreterminals 130 may be connected to the processing device 140 directly (asindicated by the bi-directional arrow in dotted lines linking the one ormore terminals 130 and the processing device 140) or through the network120, as illustrated in FIG. 1 or FIG. 2.

The IGRT apparatus 110 may be a single-modality device and/or amulti-modality (e.g., dual-modality) device that can generate a medicalimage and perform a radiation therapy (e.g., based on the medical imageof a region of interest (ROI)). In some embodiments, the medical imagemay be generated by an imaging component (also referred to as imagingdevice) of the IGRT apparatus 110. Exemplary imaging devices may includea computed tomography (CT) device, a single photon emission computedtomography (SPECT) device, a multi-modality imaging device, or the like,or any combination thereof. Exemplary CT device may include a cone beamcomputed tomography (CBCT) device. Exemplary multi-modality imagingdevices may include a computed tomography-positron emission tomography(CT-PET) device, a computed tomography-magnetic resonance imaging(CT-MRI) device, or the like. The radiation therapy may be performed bya radiotherapy component (also referred to as radiotherapy (RT) device)of the IGRT apparatus 110. Exemplary RT devices may include a linearaccelerator (LINAC), a Co-60 gamma radiator, or the like.

The “image” mentioned in the present disclosure may refer to atwo-dimensional (2D) image, a three-dimensional (3D) image, afour-dimensional (4D) image (e.g., a video, a time series of 3D images),and/or image related data (e.g., CT data, projection data correspondingto the CT data, etc.).

The IGRT apparatus 110 may include one or more diagnostic devices and/ortherapeutic devices, such as a CT device, a PET-CT device, a volume CTdevice, an RT device, or the like.

In some embodiments, the IGRT apparatus 110 may only include the RTdevice (e.g., the LINAC). As illustrated in FIG. 1, the RT device mayinclude a gantry 111, a treatment table 114, a treatment head 116, andan electron portal imaging device (EPID) 113. The gantry 111 may supportthe treatment head 116 and the EPID 113, and move (e.g., translateand/or rotate) these devices to various rotational and/or axialpositions relative to a subject (e.g., a patient, a man-made object, aspecific organ or tissues) to be examined. The subject to be examined(e.g., to be imaged and/or treated) may be placed on the treatment table114. The gantry 111 may be a ring gantry, but other types of mountingarrangements may also be employed. For example, a C-type, a partial ringgantry, or a robotic arm can be used.

The treatment head 116 of the RT device may include a target, a primarycollimator, a flattening filter, Y jaws, X jaws, and a Multi-leafCollimator (MLC), and so on. Accelerated particles (e.g., electrons) maystrike the target to produce radiation beams (e.g., photon beams orX-ray beams). The radiation beams may pass through the one or morecomponents (e.g., the primary collimator, the flattening filter, the Yjaws, X jaws, and the MCL) of the treatment head 116 to form desiredradiation beams with a certain shape, which corresponds to a shape andsize of a region of interest (ROI) in a subject (e.g., a lesion in thesubject). The radiation beams may be irradiated to the ROI for theradiation therapy. In some embodiments, the radiation beams may impingeon the EPID 113 after passing through the ROI. The EPID 113 may acquirea portal image for verifying a position of the subject, or a portionthereof (e.g., the position of the ROI in the subject), and a fieldsize.

In some embodiments, as illustrated in FIG. 1, the IGRT apparatus 110may include a CBCT device and an RT device. For example, the IGRTapparatus 110 may include the gantry 111, the treatment table 114, ascan source 115, the treatment head 116, the detector 112, and the EPID113. The gantry 111 may be configured to support the scan source 115,the treatment head 116, the detector 112, the EPID 113, and so on. Thegantry 111 may move (e.g., translate and/or rotate) these devices tovarious circumferential and/or axial positions relative to the subjectto be examined. The subject to be examined may be placed on thetreatment table 114. The scan source 115 of the CBCT device and thetreatment head 116 of the RT device may be integrated or disposedseparately. For example, the scan source 115 and the treatment head 116can be integrated to a same apparatus (e.g., two radiation sourcesimplemented in the same apparatus, or one radiation source that can emitradiation beams of different energy levels for imaging and treatment,respectively), and they are disposed at the same location, asillustrated in FIG. 1 or FIG. 2. As another example, the scan source 115and the treatment head 116 may also be disposed separately, and they aredisposed at different locations on the gantry 111, not shown in FIG. 1or FIG. 2. In some embodiments, the scan source 115 may emit a coneX-ray beam toward the subject placed on the treatment table 114. TheX-rays may be attenuated when passing through the subject. The detector112 of the CBCT device may detect at least a portion of the attenuatedX-rays. The CBCT device may generate image data based on the attenuatedX-rays detected by the detector 112.

In some embodiments, the detector 112 of the CBCT device and the EPID113 of the RT device may be integrated or disposed separately. Forexample, the detector 112 and the EPID 113 can be integrated to a sameapparatus, and disposed at the same location, as illustrated in FIG. 1or FIG. 2. As another example, the detector 112 and the EPID 113 may bedisposed separately, and they are disposed at different locations on thegantry 111, not shown in FIG. 1 or FIG. 2.

In some embodiments, the locations of the scan source 115 and thedetector 112 of the CBCT device can be disposed oppositely such that thedetector 112 may receive the imaging radiation beams from the scansource 115. The treatment head 116 and EPID 113 of the RT device can bedisposed oppositely such that the EPID 113 may receive the treatmentradiation beams from the treatment head 116.

The network 120 may include any suitable network that can facilitate theexchange of information and/or data. In some embodiments, one or morecomponents of the medical system 100 (e.g., the CBCT device, the RTdevice, the terminal(s) 130, the processing device 140, the storagedevice 150, etc.) may communicate with each other via the network 120.For example, the processing device 140 may acquire image data from theCBCT device and/or the RT device via the network 120. As anotherexample, the processing device 140 may acquire projection data (e.g.,subject-related projection data) from the CBCT device and/or the RTdevice over the network 120. As a further example, the processing device140 may obtain user instructions from the terminal 130 via the network120. The network 120 may be and/or include a public network (e.g., theInternet), a private network (e.g., a local area network (LAN), a widearea network (WAN)), etc.), a wired network (e.g., an Ethernet network),a wireless network (e.g., an 802.11 network, a Wi-Fi network, etc.), acellular network (e.g., a Long Term Evolution (LTE) network), a framerelay network, a virtual private network (“VPN”), a satellite network, atelephone network, routers, hubs, switches, server computers, and/or anycombination thereof. Merely by way of example, the network 120 mayinclude a cable network, a wireline network, a fiber-optic network, atelecommunications network, an intranet, a wireless local area network(WLAN), a metropolitan area network (MAN), a public telephone switchednetwork (PSTN), a Bluetooth™ network, a ZigBee™ network, a near fieldcommunication (NFC) network, or the like, or any combination thereof. Insome embodiments, the network 120 may include one or more network accesspoints. For example, the network 120 may include wired and/or wirelessnetwork access points such as base stations and/or internet exchangepoints through which one or more components of the medical system 100may be connected to the network 120 to exchange data and/or information.

The one or more terminals 130 may include a mobile device 131, a tabletcomputer 132, a laptop computer 133, or the like, or any combinationthereof. In some embodiments, the mobile device 131 may include a smarthome device, a wearable device, a mobile device, a virtual realitydevice, an augmented reality device, or the like, or any combinationthereof. In some embodiments, the smart home device may include smartlighting apps, smart appliance control apps, smart monitoring apps,smart TVs, smart cameras, walkie-talkies, or the like, or anycombination thereof. In some embodiments, the wearable device mayinclude a wristband, footwear, glasses, a helmet, a watch, a garment, abackpack, a smart accessory or the like, or any combination thereof. Insome embodiments, the mobile device may include a mobile phone, apersonal digital assistant (PDA), a game device, a navigation device, apoint of sale (POS) device, a laptop computer, a tablet computer, adesktop computer, or the like, or any combination thereof. In someembodiments, the virtual reality device and/or the augmented realityapparatus may include a virtual reality helmet, virtual reality glasses,a virtual reality eyewear, an augmented reality helmet, augmentedreality glasses, an augmented reality eyewear, or the like, or anycombination thereof. For example, the virtual reality device and/or theaugmented reality device may include a Google Glass™, an Oculus Rift™, aHololens™, a Gear VR™, etc. In some embodiments, the one or moreterminals 130 may be part of the processing device 140.

The processing device 140 may process data and/or information obtainedfrom the IGRT apparatus 110, the one or more terminals 130, the storagedevice 150, or other components of the medical system 100. For example,the obtained data and/or information may include imaging data related tothe subject.

In some embodiments, the processing device 140 may process the radiationdata and the attenuation coefficient distribution related to the subjectto determine composite image data after the radiation beam has passedthrough the subject. The imaging data may include the energy responsedata generated after the composite image data is projected onto thedetector. The processing device 140 may determine an energy responsefunction of the detector 112 and/or the EPID 113 based on the imagingdata and the composite image data.

In some embodiments, the processing device 140 may include a singleserver or a server group. The server group may be centralized ordistributed. In some embodiments, the processing device 140 may be localto or remote from the medical system 100. For example, the processingdevice 140 may access information and/or data from the IGRT apparatus110, the terminal 130, and/or the storage device 150 via the network120. As another example, the processing device 140 may be directlyconnected to the IGRT apparatus 110, the terminal 130, and/or thestorage device 150 to access information and/or data. In someembodiments, the processing device 140 may be implemented on a cloudplatform. For example, the cloud platform may include a private cloud, apublic cloud, a hybrid cloud, a community cloud, a distributed cloud, aninter-cloud, a multi-cloud, or the like, or a combination thereof. Insome embodiments, the processing device 140 may be implemented by acomputing device having one or more components as described inconnection with FIG. 4.

In some embodiments, the processing device 140 may include one or moreprocessors (e.g., single-core processor(s) or multi-core processor(s)).Merely by way of example, the processing device 140 may include acentral processing unit (CPU), an application-specific integratedcircuit (ASIC), an application-specific instruction-set processor(ASIP), a graphics processing unit (GPU), a physics processing unit(PPU), a digital signal processor (DSP), a field-programmable gate array(FPGA), a programmable logic device (PLD), a controller, amicrocontroller unit, a reduced instruction-set computer (RISC), amicroprocessor, or the like, or any combination thereof.

The storage device 150 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 150 may store dataobtained from one or more components of the medical system 100 (e.g.,the IGRT apparatus 110, the terminal 130). In some embodiments, thestorage device 150 may store data and/or instructions that theprocessing device 140 may execute or use to perform exemplarymethods/systems described in the present disclosure. In someembodiments, the storage device 150 may include a mass storage,removable storage, a volatile read-and-write memory, a read-only memory(ROM), or the like, or any combination thereof. Exemplary mass storagemay include a magnetic disk, an optical disk, a solid-state drive, etc.Exemplary removable storage may include a flash drive, a floppy disk, anoptical disk, a memory card, a zip disk, a magnetic tape, etc. Exemplaryvolatile read-and-write memories may include a random-access memory(RAM). Exemplary RAM may include a dynamic RAM (DRAM), a double daterate synchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), athyristor RAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. ExemplaryROM may include a mask ROM (MROM), a programmable ROM (PROM), anerasable programmable ROM (EPROM), an electrically erasable programmableROM (EEPROM), a compact disk ROM (CD-ROM), and a digital versatile diskROM, etc. In some embodiments, the storage device 150 may be implementedon a cloud platform. Merely by way of example, the cloud platform mayinclude a private cloud, a public cloud, a hybrid cloud, a communitycloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like,or any combination thereof.

In some embodiments, the storage device 150 may be connected to thenetwork 120 to communicate with one or more other components in themedical system 100 (e.g., the IGRT apparatus 110, the processing device140, and/or the terminal device(s) 130). One or more components in themedical system 100 may access the data or instructions stored in thestorage device 150 via the network 120. In some embodiments, the storagedevice 150 may be part of the processing device 140.

In some embodiments, the one or more components of the IGRT apparatus110 (such as the treatment table 114, the scan source 115, the treatmenthead 116, the detector 112, and the EPID 113, etc.) may be moved basedon control commands. The control commands may be determined based ontreatment parameters (e.g., radiation dose) in a predetermined treatmentplan and/or image information (e.g., an ROI image) or other information(e.g., feature information in the ROI image).

It should be noted that the above description of the medical system 100is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. For example, the assemblyand/or function of the medical system 100 may be varied or changedaccording to specific implementation scenarios.

FIG. 3 is a schematic diagram illustrating an exemplary radiationtherapy apparatus according to some embodiments of the presentdisclosure. The radiation therapy apparatus may be the IGRT apparatus110 illustrated in FIG. 1. As illustrated in FIG. 3, the IGRT apparatus110 may include a gantry 111, a treatment table 114, a treatment head116, and an EPID 113. In some embodiments, an imaging component (notshown in FIG. 3) may be mounted on the gantry 111. The imaging componentmay include a CT device, a magnetic resonance imaging (MRI) device, or apositron emission tomography (PET) device, or the like, or anycombination thereof.

In some embodiments, the gantry 111 may be a ring gantry with asubstantially cylindrical configuration. The gantry 111 may be disposedon a base and be rotatable on the base. The gantry 111 may include abore 101. The gantry 111 may rotate around a central axis of the bore101. A rotation axis of the gantry 111 and the central axis of the bore101 may be coaxial. The treatment head 116 and the EPID 113 may berespectively mounted on the gantry 111. In some embodiments, during theradiation therapy procedure, the treatment head 116 and the EPID 113 canbe oppositely disposed on both sides of the rotation axis.

In some embodiments, the position of the treatment table 114 may beadjusted to obtain a guide image of a subject to be examined. The IGRTapparatus 110 may perform the radiation therapy for the subject based oninformation related to the guide image. For example, the treatment table114 may be adjusted in a vertical direction in order to change adistance between the treatment table 114 and a horizontal plane (e.g.,the floor). As another example, the treatment table 114 may move alongthe rotation axis of the gantry 111. The treatment table 114 may bemoved into the bore 101 of the gantry 111, or may be moved out from thebore 101. As a further example, the treatment table 114 may be rotatedon the horizontal plane.

In some embodiments, the treatment head 116 may include one or more beamlimiting components (not shown in FIG. 3). For example, the one or morebeam limiting components may include Y jaws, X jaws, the MLC, and so on.The one or more beam limiting components may be configured to control ashape and an irradiation area of the radiation beams by adjusting thestructure of at least one beam limiting component.

In some embodiments, the EPID 113 may be a camera-based device, such asa flat plane imager with a detector array. The EPID 113 may include anarray of solid state detectors (e.g., amorphous silicon-based detectors,or dosimeters), which may record the amount of radiation that impinge onthem and convert the received amount of radiation into correspondingnumber (or count) of electrons. The electrons may be converted intoelectrical signals. A portal image may be generated based on theelectrical signals. In some embodiments, for a treatment beam associatedwith a planned radiation dose and a planned gantry angle, the radiationbeams can be irradiated onto the EPID 113, a sequence of portal imagesmay be generated accordingly. The portal images may be converted toportal dose images (PDIs). The portal dose images may characterize dosedistributions of radiation beams measured by the EPID 113.

FIG. 4 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary computing device according to someembodiments of the present disclosure. In some embodiments, theprocessing device 140 illustrated in FIG. 1 or FIG. 2 may be implementedon computing device 300 illustrated in FIG. 4. As illustrated in FIG. 4,the computing device 300 may include a processor 310, a storage 320, aninput/output (I/O) 330, and a communication port 340.

The processor 310 may execute computer instructions (program codes) andperform functions of the processing device 140 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, signals, datastructures, procedures, modules, and functions, which perform particularfunctions described herein. For example, the processor 310 may processimage data obtained from the IGRT apparatus 110, the terminals(s) 130,the storage device 150, and/or any other component of the medical system100. In some embodiments, the processor 310 may include one or morehardware processors, such as a microcontroller, a microprocessor, areduced instruction set computer (RISC), an application-specificintegrated circuits (ASICs), an application-specific instruction-setprocessor (ASIP), a central processing unit (CPU), a graphics processingunit (GPU), a physics processing unit (PPU), a microcontroller unit, adigital signal processor (DSP), a field-programmable gate array (FPGA),an advanced RISC machine (ARM), a programmable logic device (PLD), anycircuit or processor capable of executing one or more functions, or thelike, or any combinations thereof.

Merely for illustration, only one processor is described in thecomputing device 300. However, it should be noted that the computingdevice 300 in the present disclosure may also include multipleprocessors. Thus operations and/or method steps that are performed byone processor as described in the present disclosure may also be jointlyor separately performed by the multiple processors. For example, if inthe present disclosure the processor of the computing device 300executes both operation A and operation B, it should be understood thatoperation A and operation B may also be performed by two or moredifferent processors jointly or separately in the computing device 300(e.g., a first processor executes operation A and a second processorexecutes operation B, or the first and second processors jointly executeoperations A and B).

The storage 320 may store data/information obtained from the IGRTapparatus 110, the terminal(s) 130, the storage device 150, and/or anyother component of the medical system 100. In some embodiments, thestorage 320 may include a mass storage device, a removable storagedevice, a volatile read-and-write memory, a read-only memory (ROM), orthe like, or any combination thereof. For example, the mass storage mayinclude a magnetic disk, an optical disk, a solid-state drive, etc. Theremovable storage may include a flash drive, a floppy disk, an opticaldisk, a memory card, a zip disk, a magnetic tape, etc. The volatileread-and-write memory may include a random-access memory (RAM). The RAMmay include a dynamic RAM (DRAM), a double date rate synchronous dynamicRAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. The ROM may include a mask ROM (MROM),a programmable ROM (PROM), an erasable programmable ROM (PEROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage 320 may store one or more programs and/or instructions toperform exemplary methods described in the present disclosure. Forexample, the storage 320 may store programs or codes that the processingdevice 140 processes image data related to a subject.

The input/output (I/O) 330 may input or output signals, data, and/orinformation. In some embodiments, the I/O 330 may enable userinteraction with the processing device 140. In some embodiments, the I/O330 may include an input device and an output device. Exemplary inputdevices may include a keyboard, a mouse, a touch screen, a microphone,or the like, or a combination thereof. Exemplary output devices mayinclude a display device, a loudspeaker, a printer, a projector, or thelike, or a combination thereof. Exemplary display devices may include aliquid crystal display (LCD), a light-emitting diode (LED)-baseddisplay, a flat panel display, a curved screen, a television device, acathode ray tube (CRT), or the like, or a combination thereof.

The communication port 340 may be connected with a network (e.g., thenetwork 120) to facilitate data communications. The communication port340 may establish connections between the processing device 140 and theIGRT apparatus 110, the terminal 130, and/or the storage device 150. Theconnection may be a wired connection, a wireless connection, or acombination of both that enables data transmission and reception. Thewired connection may include an electrical cable, an optical cable, atelephone wire, or the like, or any combination thereof. The wirelessconnection may include a Bluetooth network, a Wi-Fi network, a WiMaxnetwork, a WLAN, a ZigBee network, a mobile network (e.g., 3G, 4G, 5G,6G, etc.), or the like, or any combination thereof. In some embodiments,the communication port 340 may be a standardized communication port,such as RS232, RS485, etc. In some embodiments, the communication port340 may be a specially designed communication port. For example, thecommunication port 340 may be designed in accordance with the digitalimaging and communications in medicine (DICOM) protocol.

FIG. 5 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary mobile device according to some embodimentsof the present disclosure. In some embodiments, the terminal 130 may beimplemented on the mobile device 131 illustrated in FIG. 5. Asillustrated in FIG. 5, the mobile device 131 may include a communicationunit (e.g., an antenna) 410, a display unit 420, a graphics processingunit (GPU) 430, a central processing unit (CPU) 440, an I/O 450, amemory 460, and a storage unit 490. In some embodiments, any othersuitable component, including but not limited to a system bus or acontroller (not shown), may also be included in the mobile device 131.In some embodiments, a mobile operating system 470 (e.g., iOS, Android,Windows Phone, Harmony OS, etc.) and one or more applications 480 may beloaded into the memory 460 from the storage unit 490 in order to beexecuted by the CPU 440. The applications 480 may include a browser orany other suitable mobile apps for receiving and rendering informationrelating to image processing or other information from the processingdevice 140. User interactions with the information stream may beachieved via the I/O 450 and provided to the processing device 140and/or other components of the medical system 100 via the network 120.

To implement various modules, units, and functionalities described inthe present disclosure, computer hardware platforms may be used as thehardware platform(s) for one or more of the elements described herein.The hardware elements, operating systems and programming languages ofsuch computers are conventional in nature, and it is presumed that thoseskilled in the art are adequately familiar therewith to adapt thosetechnologies to generate an image as described herein. A computer withuser interface elements may be used to implement a personal computer(PC) or another type of work station or terminal device, although acomputer may also act as a server if appropriately programmed. It isbelieved that those skilled in the art are familiar with the structure,programming and general operation of such computer equipment and as aresult, the drawings should be self-explanatory.

FIG. 6 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure. In someembodiments, the processing device 140 may be implemented on thecomputing device 300 (e.g., the processor 310) illustrated in FIG. 4 ora CPU 440 as illustrated in FIG. 5. As shown in FIG. 6, the processingdevice 140 may include an acquisition module 510, a calculation module520, and a storage module 530. Each of the modules described above maybe a hardware circuit that is designed to perform certain actions, e.g.,according to a set of instructions stored in one or more storage media,and/or any combination of the hardware circuit and the one or morestorage media.

The acquisition module 510 may be configured to obtain information fromone or more components (e.g., the IGRT apparatus 110, the one or moreterminals 130, the storage device 150, etc.) of the medical system 100.For example, the acquisition module 510 may obtain a plurality of rawimages with respect to measured radiation beams through the EPID 113. Asanother example, the acquisition module 510 may acquire obtain one ormore calibration parameters from a storage device (e.g., the storagedevice 150 or the storage module 530). The one or more calibrationparameters may include a position offset value, a detector gain valueand/or a curve correction value. As a further example, the acquisitionmodule 510 may obtain a predetermined treatment plan from a treatmentplan system (TPS). In accordance with the predetermined treatment plan,at a planned gantry angle, radiation beams with a planned radiation dosemay be delivered. In some embodiments, the acquisition module 510 maysend the acquired data to the calculation module 520, and/or the storagemodule 530.

The calculation module 520 may be configured to determine differencesbetween a planned radiation dose distribution and an actual radiationdose distribution (i.e., the actual dose delivered to the target duringthe radiation treatment). In some embodiments, the calculation module520 may determine a measured dose image. The calculation module 520 maydetermine an energy fluence distribution map related to radiation beamspredicted by a first portal dose prediction model. The first portal doseprediction model may simulate or predict the radiation beamscorresponding to the planned radiation dose and the planned gantryangle. In some embodiments, the first portal dose prediction model mayinclude a Monte Carlo (MC) simulation model. The calculation module 520may determine a predicted dose image based on the energy fluencedistribution map and a simulated energy response curve related to theEPID 113. In some embodiments, the simulated energy response curverelated to the EPID 113 may be determined in advance by modeling anenergy deposition efficiency of the EPID 113. The calculation module 520may determine differences between the measured and predicted dose imagesby comparing dose distributions of the measured and predicted doseimages. For example, the calculation module 520 may quantitativelyestimate the differences between the measured and predicted dose imagesbased on a gamma evaluation method. More descriptions regarding thecomparison of the measured dose image and the predicted dose image maybe found elsewhere in the present disclosure (e.g., FIG. 7 and thedescriptions thereof).

The storage module 530 may be configured to store data and/orinformation from the medical system 100. For example, the storage module530 may store the one or more calibration parameters. The storage module530 may store an output correction factor. The storage module 530 maystore an absolute dose correction factor. It should be noted that anydata or information generated during the pre-treatment QA verificationand the radiation treatment may be stored in the storage module 530.

It should be noted that the above description of the processing device140 is provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. For example, the storage module 530 may be omitted. Asanother example, the calculation module 520 may be omitted, while theIGRT apparatus 110 and/or the one or more terminals 130 may beconfigured to perform one or more functions of the calculation module520 described in the present disclosure.

FIG. 7 is a flowchart illustrating an exemplary process for verifyingradiation dose according to some embodiments of the present disclosure.In some embodiments, the process 700 illustrated in FIG. 7 may beadopted for pre-treatment quality assurance (QA) in a radiationtreatment. In some embodiments, the process 700 may be executed by themedical system 100. For example, the process 700 may be implemented as aset of instructions (e.g., an application) stored in a storage device(e.g., the storage device 150, the storage 320, and/or the storage unit490). In some embodiments, the processing device 140 (e.g., theprocessor 310 of the computing device 300, the CPU 440 of the mobiledevice 131, and/or one or more modules illustrated in FIG. 6) mayexecute the set of instructions to perform the process 700. Theoperations of the illustrated process presented below are intended to beillustrative. In some embodiments, the process 700 may be accomplishedwith one or more additional operations not described and/or without oneor more of the operations discussed. Additionally, the order of theoperations of the process 700 illustrated in FIG. 7 and described belowis not intended to be limiting.

In 702, the processing device (e.g., the calculation module 520 of theprocessing device 140) may determine a measured dose image through anelectronic portal imaging device (EPID). The measured dose image refersto a portal dose image measured by the EPID (e.g., the EPID 113) andindicates a dose distribution of radiation beams measured by the EPID.

In some embodiments, a predetermined treatment plan may be obtained froma treatment plan system (TPS) of the medical system 100. Thepredetermined treatment plan may include a radiation dose, a radiationrate, a dose rate, a radiation time, or the like, or any combinationthereof. In some embodiments, in accordance with the predeterminedtreatment plan, a treatment head of an IGRT apparatus (e.g., thetreatment head 116 of the IGRT apparatus 110) may emit and deliverradiation beams with a planned radiation dose and at a specific gantryangle. In some embodiments, the planned radiation dose can need to beverified prior to the first treatment of the subject (e.g., thepatient). The actual radiation delivery may be made based on the plannedradiation dose.

Merely by way of example, for the pre-treatment portal dosimetryverification, in accordance with the predetermined treatment plan, thetreatment head 116 may be positioned at a planned gantry angle anddeliver the radiation beams with a planned radiation dose towards theEPID 113. There is no attenuating medium between the treatment head 116and the EPID 113 during the radiation delivery. For example, during theradiation delivery, the subject and/or the treatment table may not beplaced between the treatment head 116 and the EPID 113. The radiationbeams can impinge directly on the plane of the EPID 113. The EPID 113may acquire a plurality of raw images in response to electrical signalsresulting from the incident radiation beams. The plurality of raw imagesmay be a sequence of image frames. Each image frame may be a portalimage. In some embodiments, a pixel (or voxel) value of the portal imagemay correspond to a dose value of a portal dose image. The portal doseimage may be indicative of a dose distribution of the radiation beamsimpinging on the EPID 113. In some embodiments, the measured raw imagesmay be converted to the measured dose image.

In some embodiments, the measured raw image (e.g., the measured portalimages) may be calibrated based one or more calibration parameters. Theone or more calibration parameters may include a position offset value,a detector gain value and/or a curve correction value. In someembodiments, the one or more calibration parameters may bepredetermined, for example, with reference to the descriptions of FIG.9. In some embodiments, each measured raw image may be calibrated basedon the one or more calibration parameters. The calibrated raw images maybe summarized to form a final calibrated image. The final calibratedimage may be converted to the measured dose image. In some embodiments,the measured raw images may be summarized. The summarized image may becalibrated based on the one or more calibration parameters, therebyforming a final calibrated image. The final calibrated image may beconverted to the measured dose image. More descriptions regarding thedetermination of the measured dose image may be found elsewhere in thepresent disclosure (e.g., FIGS. 8A and 8B, and the descriptionsthereof).

In 704, the processing device (e.g., the calculation module 520 of theprocessing device 140) may determine an energy fluence distribution maprelated to radiation beams predicted by a first portal dose predictionmodel.

In some embodiments, the first portal dose prediction model may simulatethe radiation delivery procedure (e.g., particle transport) between thetreatment head 116 and the EPID 113. For example, the first portal doseprediction model may simulate or predict the radiation beamscorresponding to the planned radiation dose and the planned gantryangle.

In some embodiments, the first portal dose prediction model may includea Monte Carlo (MC) simulation model. The MC simulation method, alsocalled a random sampling technique, is a computational technique that isfundamentally different from a general numerical calculation technique,and belongs to a branch of experimental mathematics. The MC simulationcan use random numbers for a statistical test and obtain statisticalfeature values (such as a mean, a probability, etc.) as numericalsolutions to the problem to be solved. In the TPS for predetermining thetreatment plan, an MC dose calculation algorithm may be applied tosample particles (e.g., photons or electrons) and transport theparticles, determine an energy deposition when the particles interactwith a reaction cross-section of different materials, generate secondaryparticles for coupled MC transport, and determine a dose of an point ofinterest (POI) or a dose distribution of an interest of region.

In some embodiments, the MC simulation model may be a geometric model ofthe whole LINAC head (e.g., the treatment head 116). In the MCsimulation model, all components (e.g., the target, the primarycollimator, the flattening filter, the jaws, and the MLC) of thetreatment head 116 may be modeled (e.g., via a MC simulation software(e.g., DOSXYZ)). In some embodiments, the MC simulation model mayfurther include a plane below the collimating system (e.g., the MLC).The plane is called a phase-space plane. A phase-space file of thephase-space plane may be obtained. The phase-space file may contain fileparameters, such as position, direction, energy or charge of allparticles hitting the phase-space plane. In some embodiments, the plane(and the file) may be used as a source for the MC transport simulation.The energy fluence distribution map may be generated based on the fileparameters of the phase-space file.

In some embodiments, a virtual source model may be constructed based onthe MC simulation technique. The virtual source model is one type of theMC simulation model. In some embodiments, the virtual source model maybe deemed as a parameterization of the phase-space file includingseveral sub-sources (e.g., the target, the primary collimator, theflattening filter, the charged particles, etc.) and serve as a particlegenerator for the MC simulation. The virtual source model may generateparticle distributions indicative of the energy fluence distribution mapusing the MC dose calculation algorithm. In some embodiments, during thesimulation of the virtual source model, a plurality of parameters (e.g.,a radius of a primary source or a secondary source, an energy spectrum,an off-axis softening coefficient, etc.) of the virtual source model maybe adjusted such that the calculated contours of different square fieldsand the percent depth dose (PDD) curves can be consistent withcorresponding measured data. In this case, the particle distributioncalculated or predicted by the virtual source model may be consistentwith the particle distribution in a radiation beam emitted from thetreatment head 116. The energy fluence distribution map of the predictedparticle distribution may be determined. It is understood that theenergy fluence distribution map is a calculated result of the MCsimulation model, and it is not an actual measured result.

In some embodiments, the energy fluence distribution map maycharacterize the particle distribution on the plane of the EPID 113, butnot the particles' entry into or reaction with the EPID 113. In someembodiments, the MC simulation model may also be used to simulate theprocess of the particles entering and reacting with EPID 113, but thesimulation process is time-consuming. Thereby, the simulation process ofthe particles entering and reacting with EPID 113 may be omitted in thedetermination of the energy fluence distribution map, which may improvethe computational efficiency in the MC simulation.

In 706, the processing device (e.g., the calculation module 520 of theprocessing device 140) may determine a predicted dose image based on theenergy fluence distribution map and a simulated energy response curverelated to the EPID (e.g., the EPID 113). The predicted dose image maybe indicative of a dose distribution of the predicted radiation beamsgenerated by the first portal dose prediction model.

In some embodiments, the simulated energy response curve related to theEPID 113 may be determined in advance by modeling an energy depositionefficiency of the EPID 113. The transport of the particles in the EPID113 may be simulated. A Monte Carlo (MC) dose engine (or the MCsimulation software) may be applied to simulate the transport of theparticles in the EPID 113. Exemplary MC dose engines may include DOSXYZ,EGS4/EGSnrc, MCNP, GEANT4, or the like. As used herein, DOSXYZ (an opensource MC simulation software) may be selected as the MC dose engine.The MC dose engine may model the detector structure of the EPID 113 andobtain the energy deposition efficiency of different-energy particlesentering the EPID 113. The EPID 113 may include an array of solid-statedetectors (e.g., amorphous silicon-based detectors, or dosimeters). Forexample, the size of the detector array may be 1024×1024. The detectorarray of the EPID 113 may be modeled through the DOSXYZ. Then the energydeposition efficiency of particles of different incident energies may bedetermined based on the modeled detector array.

FIG. 11 illustrates a simulated energy response curve related to theEPID (e.g., the EPID 113) according to some embodiments of the presentdisclosure. The simulated energy response curve may characterize thatthe energy deposited in detector varying with incident energy of theparticles. As illustrated in FIG. 11, the horizontal axis represents anincident photon energy (Mev) and the vertical axis represents an energydeposition efficiency (Mev/photon). In some embodiments, the relevantdata of the energy response curve (e.g., incident energies and/ordeposited energies in the detectors) may be stored in the form of a datatable or figure. In some embodiments, the data table may be stored in astorage device (e.g., the storage device 150 or the storage module 530).In some embodiments, the energy deposition efficiency of differentincident particles may be obtained by looking up the stored data tableor figure. By the way of looking up the data table or figure of theenergy response curve, the simulation of complex particle transportusing the MC dose algorithm may be avoided, and the computational speedand/or the accuracy of the MC dose engine may be improved.

In some embodiments, the predicted dose image may be calculated based onthe calculated energy fluence distribution map and the simulated energyresponse curve. For example, the calculation module 520 may multiply theenergy fluence distribution map by the simulated energy response curveto form the predicted dose image. The predicted dose image maycharacterize the dose distribution of the predicted radiation beamsgenerated by the first portal dose prediction model.

In 708, the processing device (e.g., the calculation module 520 of theprocessing device 140) may determine differences between the measuredand predicted dose images by comparing dose distributions of themeasured and predicted dose images. As mentioned above, the measureddose image may be indicative of the dose distribution of the radiationbeams measured by the EPID 113. The predicted dose image may beindicative of the dose distribution of the radiation beams predicted (orcalculated) by the first portal dose prediction model (e.g., the MCsimulation model). The measured radiation beams and the predictedradiation beams may be associated with the planned radiation dose. Insome embodiments, the differences between the measured dose distributionand the predicted dose distribution are an indication of radiation dosedelivery errors. According to the comparison of the measured dosedistribution and the predicted dose distribution, the processing devicemay determine whether the planned radiation dose is reasonable.

In some embodiments, the processing device 140 may quantitativelyestimate the differences between the measured and predicted dose imagesbased on a gamma evaluation technique. The gamma evaluation techniquemay combine a dose difference criterion with a distance-to-agreement(DTA) criterion. In some embodiments, the DTA is the distance between ameasured data point (e.g., a pixel in the measured dose image) and thenearest data point (e.g., a pixel in the predicted dose image) in thepredicted dose distribution that exhibits the same radiation dose. Insome embodiments, a relative dose difference between the measured doseimage and the predicted dose image may be calculated by comparing afirst data point in the measured dose distribution with a second datapoint in the predicted dose distribution.

Merely by way of example, a general representation of the gammaevaluation method for determining an acceptance criterion that considersboth the dose difference and the DTA is as follows:

$\begin{matrix}{{{\Gamma \left( {r_{p},r_{m}} \right)} = \sqrt{\frac{r^{2}\left( {r_{p},r_{m}} \right)}{\Delta \; d^{2}} + \frac{\delta^{2}\left( {r_{p},r_{m}} \right)}{\Delta \; D^{2}}}},} & (1) \\{{{{whereas}\mspace{14mu} {r\left( {r_{p},r_{m}} \right)}} = \sqrt{{\Delta \; x_{p - m}^{2}} + {\Delta \; y_{p - m}^{2}}}},} & (2) \\{{{{and}\mspace{14mu} {\delta \left( {r_{p},r_{m}} \right)}} = {{D_{p}\left( r_{p} \right)} - {D_{m}\left( r_{m} \right)}}},} & (3)\end{matrix}$

where Γ represents a gamma value; r represents a spatial distancebetween a predicted data point (pixel or voxel) r_(p) in the predicteddose distribution and a measured data point r_(m) in the measured dosedistribution; x_(p) and x_(m) represent the locations along the X axisof the predicted and measured data points (i.e., r_(p) and r_(m)),respectively; y_(p) and y_(m) represent the locations along the Y axisof the predicted and measured data points (i.e., r_(p) and r_(m)),respectively; Δx_(p-m) represents the location difference between x_(p)and x_(m); Δy_(p-m) represents the location difference between y_(p) andy_(m); δ represents a dose difference between the predicted and measureddata points; D_(p) represents the predicted dose value of the predicteddata point r_(p), and D_(m) represents the measured dose value of themeasured point r_(m); ΔD represents a dose difference criteria and Δdrepresents a DTA criteria, for example, ΔD=3% and Δd=3 mm.

In some embodiments, a gamma index belonging to a predicted data pointr_(p) may be determined based on γ function as follows:

γ(r _(p))=min{Γ(r _(p) ,r _(m))}∀{r _(m)}.  (4)

According to the γ function, a minimum generalized gamma value Γ may bechosen among *** as the gamma index for the predicted data point r_(p).In some embodiments, a statistical gamma pass rate may be determinedbased on a pass-fail criterion. The pass-fail criteria may be defined asfollows:

$\begin{matrix}\left\{ {\begin{matrix}{{{\gamma \left( r_{p} \right)} \leq 1},{{calculation}\mspace{14mu} {passes}}} \\{{{{\gamma \left( r_{p} \right)} > 1},{{calculation}\mspace{14mu} {fails}}}\mspace{20mu}}\end{matrix}.} \right. & (5)\end{matrix}$

When the gamma index is greater than 1, the dose calculation may beconsidered as “fail.” When the gamma index is less than or equal to 1,the dose calculation may be considered as “pass.” The gamma pass ratemay be calculated based on a ratio of the number of the passed datapoints to all measured data points. It should be noted that, in someembodiments, when the gamma index is equal to 1, the dose calculationmay be considered as “fail.”

In some embodiments, the processing device 140 may select a plurality ofspecific measured points in the measured dose image. For example, theplurality of specific measured points may include all data points in themeasured dose image. As another example, the plurality of specificmeasured points may include a portion of the data points in the measureddose image, such as 50×50 pixels. The processing device may determinethe gamma pass rate based on the ratio of the number (or count) of thepassed data points to the number (or count) of all the measured datapoints. For example, given that the number (or count) of the specificmeasured points is 2,500 and the number (or count) of the passed datapoints is 2,400, the gamma pass rate is 96% (i.e., 2400/2500). In someembodiments, if the gamma pass rate exceeds a threshold value (e.g.,98%), the dose differences between the measured dose image and thepredicted dose image may be deemed negligible, that is, the radiationdelivery errors may be neglected. The predetermined treatment plan maybe considered as reasonable. If the gamma pass rate is below thethreshold value, the dose differences between the measured dose imageand the predicted dose image may be considered exceeding the acceptancecriterion of the radiation delivery errors. The predetermined treatmentplan may be considered as unreasonable, which may result in aninaccurate radiation therapy. The predetermined treatment plan may needto be adjusted in order to eliminate or reduce the radiation dosedelivery errors.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, insome embodiments, the determinations of the measured dose image and thepredicted dose image may be performed simultaneously, such as operation702 being performed simultaneously with operations 704-706.

FIGS. 8A and 8B illustrate exemplary processes for determining ameasured dose image according to some embodiments of the presentdisclosure. The determined measured dose image may be indicative of adose distribution of radiation beams measured by an EPID (e.g., the EPID113). In some embodiments, the process illustrated in FIG. 8A or 8B maybe executed by the medical system 100. For example, the processillustrated in FIG. 8A or 8B may be implemented as a set of instructions(e.g., an application) stored in a storage device (e.g., the storagedevice 150, the storage 320, and/or the storage unit 490). In someembodiments, the processing device 140 (e.g., the processor 310 of thecomputing device 300, the CPU 440 of the mobile device 131, and/or oneor more modules illustrated in FIG. 6) may execute the set ofinstructions to perform the process illustrated in FIG. 8A or 8B. Theoperations of the illustrated process presented below are intended to beillustrative. In some embodiments, the process illustrated in FIG. 8A or8B may be accomplished with one or more additional operations notdescribed and/or without one or more of the operations discussed.Additionally, the order of the operations of the process illustrated inFIG. 8A or 8B and described below is not intended to be limiting.

FIG. 8A is a flowchart illustrating an exemplary process for determininga measured dose image according to some embodiments of the presentdisclosure.

In 802, the processing device (e.g., the acquisition module 510 of theprocessing device 140) may obtain a plurality of raw images with respectto measured radiation beams through an EPID (e.g., the EPID 113). Asused herein, the measured radiation beams refer to radiation beamsdetected or measured by the EPID 113.

As described in connection with operation 702 illustrated in FIG. 7, inaccordance with the predetermined treatment plan, the treatment head 116may be positioned at a planned gantry angle and deliver the radiationbeams with a planned radiation dose towards the EPID 113. There is noattenuating medium between the treatment head 116 and the EPID 113during the radiation delivery. For example, during the radiationdelivery, the subject and/or the treatment table may not be placedbetween the treatment head 116 and the EPID 113. The radiation beams canimpinge directly on the plane of the EPID 113. The EPID 113 may acquirethe plurality of raw images in response to electrical signals resultingfrom the incident radiation beams. The plurality of raw images may be asequence of image frames. Each image frame may be a portal image.

In 804, the processing device (e.g., the acquisition module 510 of theprocessing device 140) may obtain one or more calibration parameters.The one or more calibration parameters may include a position offsetvalue, a detector gain value, and/or a curve correction value. In someembodiments, the one or more calibration parameters may bepredetermined, for example, with reference to the descriptions of FIG.9.

In 806, the processing device (e.g., the calculation module 520 of theprocessing device 140) may calibrate, based on the one or morecalibration parameters, each of the plurality of raw images.

In some cases, the centers of the portal image and the EPID may misaligndue to some unavoidable errors (e.g., assembly errors of the EPID 113).In some embodiments, the processing device may align the centers of theportal image and the EPID 113 based on the position offset value. Forillustrative purposes, the position offset value can be represented by(Δx, Δy), where Δx and Δy represent a position deviation of X-coordinateand Y-coordinate of the portal image and the EPID 113, respectively. Thecoordinates of a pixel in the portal image can be represented by (x, y),where x and y represent X-coordinate and Y-coordinate of the portalimage, respectively. To align the centers of the portal image and theEPID 113, the processing device 140 may calibrate the coordinates ofeach pixel of the portal image based on the position offset value. Thecoordinates of each pixel of the calibrated portal image can beexpressed as (x+Δx, y+Δy). It is understood that, through the positioncalibration for each portal image, respective centers of the pluralityof portal images may be aligned with the center of the EPID 113. As usedherein, the calibrated portal image through the position offset may bedesignated as a first calibrated image.

In some embodiments, to eliminate or reduce the sensitivity differenceof the detectors of the EPID 113 for the particles of differentenergies, the processing device may process the first calibrated imageusing the detector gain value. In some embodiments, the detector gainvalue may be in the form of a matrix, that is, the detector gain matrix.The dimensions of the detector gain matrix and the first calibratedimage may be equal, such as 1024×1024. In some embodiments, theprocessing device may divide the first calibrated image by the detectorgain matrix to form a second calibrated image.

In some embodiments, to eliminate the over-response of the detectors ofthe EPID 113 for the particles of low energy, the processing device mayprocess the second calibrated image using the curve correction value. Insome embodiments, the curve correction value may be in the form of amatrix as well, that is, the curve correction matrix. The dimensions ofthe curve correction matrix and the second calibrated image may beequal, such as 1024×1024. In some embodiments, the processing device maymultiply the second calibrated image by the curve correction matrix toform a third calibrated image. More descriptions regarding thedetermination of the one or more calibration parameters may be foundelsewhere in the present disclosure (e.g., FIG. 9 and the descriptionsthereof).

In 808, the processing device (e.g., the calculation module 520 of theprocessing device 140) may form a final calibrated image based on theplurality of calibrated raw images. As described above, each raw image(e.g., each portal image) may be calibrated to form a correspondingthird calibrated image based on the position offset value, the detectorgain matrix and the curve correction matrix. In some embodiments, theplurality of third calibrated images may be summarized to from the finalcalibrated image. In the final calibrated image, a pixel value may beequal to a sum of the values of pixels at the same pixel location ineach third calibrated image.

In 810, the processing device (e.g., the calculation module 520 of theprocessing device 140) may convert the final calibrated image to ameasured dose image. The measured dose image may be a portal dose image(PDI) indicative of the dose distribution at the plane of the EPID 113.In some embodiments, the processing device may convert the finalcalibrated image to the portal dose image using a portal dosereconstruction algorithm (e.g., Monte Carlo dose calculation algorithm).For example, grey-scale pixel values of the final calibrated image maybe converted to dose values of the measured dose image.

FIG. 8B is a flowchart illustrating an exemplary process for determininga measured dose image according to some embodiments of the presentdisclosure.

In 822, the processing device (e.g., the acquisition module 510 of theprocessing device 140) may obtain a plurality of raw images with respectto measured radiation beams through an EPID (e.g., the EPID 113). Asused herein, the measured radiation beams refer to radiation beamsdetected or measured by the EPID 113.

As described in connection with operation 702 illustrated in FIG. 7, inaccordance with the predetermined treatment plan, the treatment head 116may be positioned at a planned gantry angle and deliver the radiationbeams with a planned radiation dose towards the EPID 113. There is noattenuating medium between the treatment head 116 and the EPID 113during the radiation delivery. For example, during the radiationdelivery, the subject and/or the treatment table may not be placedbetween the treatment head 116 and the EPID 113. The radiation beams canbe impinge directly on the plane of the EPID 113. The EPID 113 mayacquire the plurality of raw images in response to electrical signalsresulting from the incident radiation beams. The plurality of raw imagesmay be a sequence of image frames. Each image frame may be a portalimage.

In 824, the processing device (e.g., the acquisition module 510 of theprocessing device 140) may obtain one or more calibration parameters.The one or more calibration parameters may include a position offsetvalue, a detector gain value and/or a curve correction value. In someembodiments, the one or more calibration parameters may bepredetermined, for example, with reference to the descriptions of FIG.9.

In 826, the processing device (e.g., the calculation module 520 of theprocessing device 140) may summarize the plurality of raw images (e.g.,the portal images). For example, for each portal image, the values ofpixels at the same pixel location may be summed. In the summarized rawimage (SRI), a pixel value may be equal to a sum of the values of pixelsat the same pixel location in each portal image.

In 828, the processing device (e.g., the calculation module 520 of theprocessing device 140) may form a final calibrated image by calibrating,based on the one or more calibration parameters, the summarized rawimage (e.g., the sum of the raw images).

In some embodiments, the processing device may align the centers of theSRI and the EPID 113 based on the position offset value. Forillustrative purposes, the position offset value can be represented by(Δx, Δy), where Δx and Δy represent a position deviation of X-coordinateand Y-coordinate of the SRI and the EPID 113, respectively. Thecoordinates of a pixel in the SRI can be represented by (x, y), where xand y represent X-coordinate and Y-coordinate of the SRI, respectively.To align the centers of SRI and the EPID 113, the processing device 140may calibrate the coordinates of each pixel of the SRI based on theposition offset value. The coordinates of each pixel of the calibratedSRI can be expressed as (x+Δx, y+Δy). It is understood that, through theposition calibration for the SRI, the centers of the calibrated EPI andthe EPID 113 may be aligned. As used herein, the calibrated SRI may bedesignated a fourth calibrated image.

In some embodiments, to eliminate the sensitivity difference of thedetectors of the EPID 113 for the particles of different energies, theprocessing device may process the fourth calibrated image using thedetector gain value. In some embodiments, the detector gain value may bein the form of a matrix, that is, the detector gain matrix. Thedimensions of the detector gain matrix and the fourth calibrated imagemay be equal, such as 1024×1024. In some embodiments, the processingdevice may divide the fourth calibrated image by the detector gainmatrix to form a fifth calibrated image.

In some embodiments, to eliminate the over-response of the detectors ofthe EPID 113 for the particles of low energy, the processing device mayprocess the fifth calibrated image using the curve correction value. Insome embodiments, the curve correction value may be in the form of amatrix as well, that is, curve correction matrix. The dimensions of thecurve correction matrix and the second calibrated image may be equal,such as 1024×1024. In some embodiments, the processing device maymultiply the fifth calibrated image by the curve correction matrix toform the final calibrated image. More descriptions regarding thedetermination of the one or more calibration parameters may be foundelsewhere in the present disclosure (e.g., FIG. 9 and the descriptionsthereof).

In 830, the processing device (e.g., the calculation module 520 of theprocessing device 140) may convert the final calibrated image to ameasured dose image. The measured dose image may be a portal dose image(PDI) indicative of the dose distribution at the plane of the EPID 113.In some embodiments, the processing device may convert the finalcalibrated image to the portal dose image using a portal dosereconstruction algorithm (e.g., Monte Carlo dose calculation algorithm).For example, grey-scale pixel values of the final calibrated image maybe converted to dose values of the measured dose image.

It should be noted that the above description of FIG. 8A or FIG. 8B ismerely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. For example, in FIG. 8A or 8B, operations 802-804 oroperations 822-824 may be integrated into a single operation.

FIG. 9 is a flowchart illustrating an exemplary process for determiningcalibration parameters according to some embodiments of the presentdisclosure. In some embodiments, the calibration parameters may includea position offset value, a detector gain value and a curve correctionvalue described in FIG. 7, and FIGS. 8A-8B. The calibrated parametersmay be used to calibrate a plurality of raw images (e.g., portal images)generated by the EPID 113. In some embodiments, the process illustratedin FIG. 9 may be executed by the medical system 100. For example, theprocess illustrated in FIG. 9 may be implemented as a set ofinstructions (e.g., an application) stored in a storage device (e.g.,the storage device 150, the storage 320, and/or the storage unit 490).In some embodiments, the processing device 140 (e.g., the processor 310of the computing device 300, the CPU 440 of the mobile device 131,and/or one or more modules illustrated in FIG. 6) may execute the set ofinstructions to perform the process illustrated in FIG. 9. Theoperations of the illustrated process presented below are intended to beillustrative. In some embodiments, the process illustrated in FIG. 9 maybe accomplished with one or more additional operations not describedand/or without one or more of the operations discussed. Additionally,the order of the operations of the process illustrated in FIG. 9 anddescribed below is not intended to be limiting.

In 902, the processing device (e.g., the calculation module 520 of theprocessing device 140) may determine position deviations of firstmeasured flood-field images relative to a center of an EPID (e.g., theEPID 113). The determined position deviations may be used to determinethe position offset value. As used herein, the measured flood-fieldimage may refer to a portal image measured (or acquired) by the EPID 113with the flood field. For example, the flood-field image may be acquiredwhile irradiating the EPID 113 with an open field of 35×35 cm². Theflood field needs to be large enough to cover the entire sensitive areaof the detectors in the EPID 113. For example, each first measuredflood-field image is a flood-field image with a 10×10 cm² field when thesensitive area of all the detectors in the EPID 113 is ***.

In some embodiments, the size of an opening or aperture of a beamlimiting component (e.g., jaws or MLC) of the treatment head 116 may beadjusted to obtain a large open field. The radiation beams can reach theEPID 113 through the opening of the beam limiting component. The largeopen field can be called the flood field. The flood-field image can bemeasured by the EPID 113 with the flood field. In some cases, assemblyerrors of the treatment head 116 and the EPID 113 may result in amisalignment of the centers of the radiation beams and a flat panelimager of the EPID 113. To resolve the misalignment, in someembodiments, the processing device may determine the position deviationsof the measured flood-field image(s) relative to the EPID 113.

Merely by way of example, the EPID 113 includes a flat panel imager with1024×1024 pixels, and the dimension of the measured flood-field image is1024×1024 pixels, as well. A pixel grey-scale value of the measuredflood-field image may be indicative of an incident radiation dose value.The coordinates of the center of the EPID 113 is (512, 512). In someembodiments, the center of the measured portal image may be aligned tothe center of the EPID 113 by calibrating the position deviationsbetween them as exemplified below.

In some embodiments, the EPID 113 may be configured to acquire the firstflood-field images with a 10×10 cm² field at collimator angles of 0° and180°, respectively. For example, the MLC of the treatment head 116 maybe positioned at 0° and 180°, respectively. The flood field of 10×10 cm²may be set through the beam limiting component (e.g., opening the jaws).The treatment head 116 may deliver radiations onto the EPID 113. TheEPID 113 may measure the first flood-field images corresponding to thecollimator angle of 0° and 180°, respectively. In an isocenter plane,Crossline, a dose curve indicative of the dose that is perpendicular tothe movement direction of the treatment table and passes through thecenter of the beam, may be measured. Inline, a dose curve indicative ofthe dose that is along the movement direction of the treatment table andpasses through the center of the beam, may be measured. In someembodiments, the processing device may determine position deviations ofthe centers of the Crossline and Inline relative to the center of theflat panel imager of the EPID 113. The position deviations may be usedto determine the position offset value of the measured portal imagerelative to the EPID 113. The position offset value, (Δx, Δy), may bedetermined as follows:

Δx=(xcenter_collimator0+xcenter_collimator180)/2,  (6)

Δy=(ycenter_collimator0+ycenter_collimator180)/2,  (7)

where xcenter_collimator0 represents a position deviation of the centerof Crossline relative to the center of the flat panel imager atcollimator angle of 0°, xcenter_collimator180 represents a positiondeviation of the center of Crossline relative to the center of the flatpanel imager at collimator angle of 180°, ycenter_collimator0 representsa position deviation of the center of Inline relative to the center ofthe flat panel imager at collimator angle of 0°, andycenter_collimator180 represents a position deviation of the center ofInline relative to the center of the flat panel imager at collimatorangle of 180°.

In some embodiments, the position offset value may be determined inadvance according to Equations 6 and 7. The position offset value may beused to calibrate the measured portal image. For example, thecalculation module 520 may align the centers of the measured portalimage and the EPID 113 based on the determined position offset value.

In 904, the processing device (e.g., the calculation module 520 of theprocessing device 140) may determine the detector gain value based on asecond measured flood-field image and a beam profile value.

In some embodiments, the second measured flood-field image may beacquired by the EPID 113. The second measured flood-field image may bedivided to a series of concentric rings. The beam profile value may beequal to an average of pixel values of the series of concentric rings.The detector gain value may be used to eliminate the sensitivitydifference of the detectors of the EPID 113 for the particles ofdifferent energies.

Merely by way of example, the EPID 113 may be configured to acquire thesecond measured flood-field image with a 10×10 cm² at SID=100 cm. TheSID may refer to a distance between a radiation source (e.g., thetarget) of the treatment head 116 to the flat panel imager of the EPID113. For example, the SID may be set as 100 cm by adjusting positions ofthe treatment head 116 and the EPID 113. The flood field of 40×40 cm²may be set through the beam limiting component (e.g., opening the jaws).The treatment head 116 may deliver radiations onto the EPID 113. TheEPID 113 may acquire the second flood-field image, that is, the secondmeasured flood-field image. The processing device may divide the secondmeasured flood-field image into a series of concentric rings. Theprocessing device may determine an average of pixel values of the seriesof concentric rings. The beam profile value may be equal to the average.Theoretically, a radiation beam is center-symmetrical with respect toits central axis. Therefore, the dose distributions in the concentricrings may be equal.

In some embodiments, the processing device may determine the detectorgain value by dividing the second measured flood-field image by the beamprofile value. For example, detector_gain_value=Flood Raw Image/BeamProfile, where “Flood Raw Image” represents image data of the secondmeasured flood-field image, and “Beam Profile” represents the beamprofile value. The image data may be a matrix, and each element of thematrix is a value of a pixel of the second measured flood-field image.In some embodiments, the processing device may divide the measuredportal image by the detector gain value in order to calibrate themeasured portal image.

In some embodiments, the processing device may determine the detectorgain value by dividing the beam profile value by the second measuredflood-field image. For example, detector_gain_value=Beam Profile/FloodRaw Image. In some embodiments, the processing device may multiply themeasured portal image by the detector gain value in order to calibratethe measured portal image.

In some embodiments, the number (or count) of the concentric rings maybe various, and not be limited to what are exemplified herein. It isunderstood that, the more the number (or count) of concentric rings andthe smaller a width of each ring, the more accurate the detector gainvalue.

In some embodiments, operation 902 and operation 904 may be performedsimultaneously or sequentially. For example, in operation 904, beforedetermining the beam profile value, the centers of the second measuredflood-field image and the EPID 113 may be aligned through the determinedposition offset value.

In 906, the processing device (e.g., the calculation module 520 of theprocessing device 140) may determine the curve correction value based ona third measured flood-field image and a predicted flood-field image.

In some embodiments, the curve correction curve may be in the form of amatrix, such as the curve correction matrix. The curve correction matrixmay be used to eliminate the over-response of the detectors of the EPID113 for the particles of low energy. In some embodiments, the thirdmeasured flood-field image may be determined based on the secondmeasured flood-field image, the position offset, and the detector gainvalue. For example, the second measured flood-field image may be alignedto the center of the EPID 113 to form an intermediate flood-field image.The third measured flood-field image may be determined by dividing theintermediate flood-field image by the detector gain value.

In some embodiments, the predicted flood-field image may be a simulatedor calculated flood-field image generated by a second portal doseprediction model. The second portal dose prediction model may include anMC simulation model (e.g., a virtual source model) similar to the firstportal dose prediction model. Compared with the first portal doseprediction model, the opening size of the beam limiting component (e.g.,the jaws or the MCL) modeled in the second portal dose prediction modelmay be different, and accordingly the field size of the open field maybe different as well. Merely for illustration, in the second portal doseprediction model, the open field may be equal to 40×40 cm², and the SIDmay be set as 100 cm.

In some embodiments, the processing device may determine the curvecorrection value by dividing the predicted flood-field image by thethird measured flood-field image. For example, curve_correction=FloodCalculated Image/Flood Raw Image, where curve_correction represents thecurve correction value, “Flood Calculated Image” represents thepredicted flood-field image, and “Flood Raw Image” represents the thirdmeasured flood-field image. In some embodiments, the processing devicemay multiply the measured portal image by the curve correction value inorder to calibrate the measured portal image.

In some embodiments, the processing device may determine the curvecorrection value may be determined by dividing the third measuredflood-field image by the predicted flood-field image. For example,curve_correction=Flood Raw Image/Flood Calculated Image. In someembodiments, the processing device may divide the measured portal imageby the curve correction value in order to calibrate the measured portalimage.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example,operation 902 and operation 904 may be performed independently. In otherwords, the position offset value and the detector gain value may bedetermined, respectively.

FIG. 10 is a flowchart illustrating an exemplary process for determininga predicted dose image according to some embodiments of the presentdisclosure. The predicted dose image may be used to measure radiationdelivery errors between the actual radiation delivery and the plannedradiation delivery. In some embodiments, the process 1000 may beexecuted by the medical system 100. For example, the process 1000 may beimplemented as a set of instructions (e.g., an application) stored in astorage device (e.g., the storage device 150, the storage 320, and/orthe storage unit 490). In some embodiments, the processing device 140(e.g., the processor 310 of the computing device 300, the CPU 440 of themobile device 131, and/or one or more modules illustrated in FIG. 6) mayexecute the set of instructions to perform the process 1000. Theoperations of the illustrated process presented below are intended to beillustrative. In some embodiments, the process 1000 may be accomplishedwith one or more additional operations not described and/or without oneor more of the operations discussed. Additionally, the order of theoperations of the process 1000 illustrated in FIG. 10 and describedbelow is not intended to be limiting.

In 1002, the processing device (e.g., the acquisition module 510 of theprocessing device 140) may obtain a predicted output factor of a firstportal dose prediction model. The first portal dose prediction model maybe the MC simulation model (or the virtual source model) described withreference to FIG. 7. The first portal dose prediction model may be modelthe whole LINAC head geometry (e.g., the treatment head 116) andsimulate the radiation delivery of the LINAC (e.g., particle transportbetween the treatment head 116 and the EPID 113). In some embodiments,the output factor of electron beams is an important parameter forradiation dose calculation. The predicted output factor (also referredto as calculated output factor) may be calculated using the MCsimulation. For example, the processing device 140 may calculate theoutput factor of the delivered electron beams simulated by the virtualsource model using the MC dose calculation algorithm. In some cases, dueto the difference in energy response for an ionization chamber and theEPID 113, the predicted output factor and the measured output factor maybe different. Therefore, the predicted output factor needs to becorrected based on an output correction factor.

In 1004, the processing device (e.g., the calculation module 520 of theprocessing device 140) may correct the predicted output factor of thefirst dose prediction model based on an output correction factor. Insome embodiments, the processing device may obtain the output correctionfactor by looking up an output correction factor table. The outputcorrection factor table may be determined in advance based on ratios ofpredicted output factors to measured output factors for different squarefields. For instance, the output correction factor table may bedetermined, maintained, and/or updated by the manufacturer of the RTapparatus, a vendor that is responsible for maintaining the RTapparatus, etc.; the manufacturer or the vendor may load the outputcorrection factor table may be determined on a storage device (e.g., thestorage device 150, the storage module 530, or an external storagedevice that the medical system 100 or a portion thereof (the processingdevice 140) may access).

In some embodiments, a virtual source model may be constructed. The SIDof the virtual source model may be set as 100 cm. The descriptionregarding the virtual source model may be found in the description withreference with FIG. 7, and is not repeated here.

In some embodiments, the processing device may initial all outputcorrection factors in the output correction factor table. For example,the output correction factors may be initialized to 1. In someembodiments, the processing device may invoke the virtual source modelto calculate the predicted output factors for a sequence of squarefields. In some embodiments, the processing device may obtain themeasured output factors for the sequence of square fields. The measuredoutput factors may be acquired by the EPID 113. For example, the EPID113 may acquire portal images for the sequence of square fields. Theoutput factors corresponding to the portal images may be measured. Insome embodiments, the portal images may be calibrated based on the oneor more calibration parameters (e.g., the position offset value, thedetector gain value and the curve correction value). The measured outputfactors may be determined based on the calibrated portal images. In someembodiments, for each of the sequence of square fields, the processingdevice may determine a ratio of the measured output factor to thepredicted output factor. The ratio may be designate as the outputcorrection factor. The output correction factors for different squarefields may be recorded in a data table (i.e., the output correctionfactor table). Through looking up the output correction factor table,the processing device 140 may quickly obtain an output correction factorcorresponding to a specific square field. The obtained output correctionfactor may be used to correct the predicted output factor of the firstportal dose prediction model.

In 1006, the processing device (e.g., the calculation module 520 of theprocessing device 140) may determine an energy fluence distribution mapby feeding the corrected output factor to the first portal doseprediction model. The energy fluence distribution map may characterizeparticles distribution irradiated onto the plane of the EPID 113.

In 1008, the processing device (e.g., the calculation module 520 of theprocessing device 140) may determine an intermediate predicted doseimage based on the energy fluence distribution map and a simulatedenergy response curve. As described in connection with FIG. 7, thesimulated energy response curve (e.g., the simulated energy responsecurve illustrated in FIG. 11) related to the EPID 113 may be determinedby simulating an energy deposition efficiency of the EPID 113. Theprocessing device may multiple the energy fluence distribution map bythe simulated energy response curve to determine the intermediatepredicted dose image.

In 1010, the processing device (e.g., the calculation module 520 of theprocessing device 140) may determine a predicted dose image bycorrecting the intermediate predicted dose image using an absolute dosecorrection factor. The predicted dose image may be indicative of a dosedistribution of the radiation beams calculated or predicted by the firstportal dose prediction model.

In some embodiments, the use of the absolute dose correction factor mayfacilitate to unify units (e.g., pixel values) of the predicted portaldose image and the measured portal dose image. In some embodiments, theabsolute dose correction factor may be determined based on a predictedflood-field image and a measured flood-field image. For example, theprocessing device 140 may obtain the predicted flood-field image with10×10 cm² field generated by the second portal dose prediction model.The processing device 140 may obtain the measured flood-field imageacquired by the EPID 113 with the same field size. The processing device140 may select a first image patch of 16×16 pixels centered in thepredicted flood-field image. The average of pixel values of the firstimage patch may be designated as a predicted dose of the predictedflood-field image. The processing device 140 may select a second imagepatch of 16×16 pixels centered in the measured flood-field image. Theaverage of voxel values of the second image patch may be designated as ameasured dose of the measured flood-field image. The processing device140 may determine a ratio of the measured dose to the predicted dose.The determined ratio may be designated as the absolute dose correctionfactor. In some embodiments, the processing device 140 may multiply theintermediate predicted dose image by the absolute dose correction factorin order to form the predicted dose image and unify the units of thepredicted dose image and the measured dose image.

In some embodiments, the processing device may determine a ratio of thepredicted dose to the measured dose. The determined ratio may bedesignated as the absolute dose correction factor. In some embodiments,the processing device 140 may divide the intermediated predicted doseimage by the absolute dose correction factor in order to form thepredicted dose image and unify the units of the predicted dose image andthe measured dose image.

In some embodiments, the absolute dose correction factor may be used tocorrect the measured dose image. For example, the absolute dosecorrection factor is equal to the ratio of the measured dose to thepredicted dose. In some embodiments, the processing device 140 maydivide the measured dose image by the absolute dose correction factor inorder to unify the units of the predicted dose image and the measureddose image.

In some embodiments, a reference dose image may be used to unify theunits of the predicted dose image and the measured dose image. Thereference dose image may be a normalized dose image acquired by the EPID113. For example, a first absolute dose correction factor may bedetermined by comparing the measured flood-field image and the referencedose image. A second absolute dose correction factor may be determinedby comparing the predicted flood-field image and the reference doseimage. The first absolute dose correction factor may be used to correctthe measured dose image. The second dose correction factor may be usedto correct the predicted dose image. The units of the predicted doseimage and the measured dose image may be unified through the correctionof the first and second absolute correction factors.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example,operation 1002 and operation 1004 may be integrated into a singleoperation. As another example, operation 1008 and operation 1010 may beintegrated into a single operation.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment,” “one embodiment,” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “block,” “module,” “engine,” “unit,” “component,” or“system.” Furthermore, aspects of the present disclosure may take theform of a computer program product embodied in one or more computerreadable media having computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C #, VB.NET, Python or the like, conventional procedural programming languages,such as the “C” programming language, Visual Basic, Fortran 1703, Perl,COBOL 1702, PHP, ABAP, dynamic programming languages such as Python,Ruby and Groovy, or other programming languages. The program code mayexecute entirely on the user's computer, partly on the user's computer,as a stand-alone software package, partly on the user's computer andpartly on a remote computer or entirely on the remote computer orserver. In the latter scenario, the remote computer may be connected tothe user's computer through any type of network, including a local areanetwork (LAN) or a wide area network (WAN), or the connection may bemade to an external computer (for example, through the Internet using anInternet Service Provider) or in a cloud computing environment oroffered as a service such as a software as a service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations, therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software-only solution—e.g., an installation onan existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various embodiments. This method ofdisclosure, however, is not to be interpreted as reflecting an intentionthat the claimed subject matter requires more features than areexpressly recited in each claim. Rather, claimed subject matter may liein less than all features of a single foregoing disclosed embodiment.

What is claimed is:
 1. A system, comprising: at least one storage deviceincluding a set of instructions; and at least one processor configuredto communicate with the at least one storage device, wherein whenexecuting the set of instructions, the at least one processor isconfigured to direct the system to perform operations including:determining a measured dose image through an electronic portal doseimaging device (EPID), the measured dose image indicating a dosedistribution of radiation beams measured by the EPID, and the measuredradiation beams corresponding to a planned radiation dose and a plannedgantry angle; determining an energy fluence distribution map related toradiation beams predicted by a first portal dose prediction model, thepredicted radiation beams corresponding to the planned radiation doseand the planned gantry angle; determining a predicted dose image basedon the energy fluence distribution map and a simulated energy responsecurve related to the EPID, the predicted dose image indicating a dosedistribution of the predicted radiation beams; and determiningdifferences between the measured dose image and the predicted dose imageby comparing the dose distributions of the measured dose image and thepredicted dose image.
 2. The system of claim 1, wherein the determininga measured dose image includes: obtaining a plurality of raw images withrespect to the measured radiation beams through the EPID; obtaining oneor more calibration parameters; calibrating, based on the one or morecalibration parameters, each of the plurality of raw images; forming afinal calibrated image based on the plurality of calibrated raw images;and converting the final calibrated image to the measured dose image. 3.The system of claim 1, wherein the determining a measured dose imageincludes: obtaining a plurality of raw images with respect to themeasured radiation beams through the EPID; obtaining one or morecalibration parameters; summarizing the plurality of raw images; forminga final calibrated image by calibrating, based on the one or morecalibration parameters, the summarized raw image; and converting thefinal calibrated image to the measured dose image.
 4. The system ofclaim 3, wherein the one or more calibration parameters include at leastone of a position offset value, a detector gain value, or a curvecorrection value.
 5. The system of claim 4, wherein the at least oneprocessor is further configured to direct the system to perform theoperations including: determining the position offset based on positiondeviations of first measured flood-field images relative to a center ofthe EPID; determining the detector gain value based on a second measuredflood-field image and a beam profile value; and determining the curvecorrection value based on a third measured flood-field image and apredicted flood-field image, wherein the third measured flood-fieldimage is associated with the second measured flood-field image, and thepredicted flood-field image is generated using a second portal doseprediction model.
 6. The system of claim 5, wherein the first portaldose prediction model or the second portal dose prediction modelincludes a Monte Carlo (MC) simulation model.
 7. The system of claim 1,wherein the determining an energy fluence distribution map related toradiation beams predicted by a first portal dose prediction modelincludes: correcting a predicted output factor of the first portal doseprediction model based on an output correction factor; and determiningthe energy fluence distribution map by feeding the corrected outputfactor to the first portal dose prediction model.
 8. The system of claim7, wherein the determining a predicted dose image based on the energyfluence distribution map and a simulated energy response curve relatedto the EPID includes: determining an intermediate predicted dose imagebased on the energy fluence distribution map and the simulated energyresponse curve; and determining the predicted dose image by correctingthe intermediate predicted dose image using an absolute dose correctionfactor.
 9. The system of claim 1, wherein the simulated energy responsecurve related to the EPID is determined in advance by modeling an energydeposition efficiency of the EPID.
 10. A method implemented on acomputing device having at least one processor and at least one storagedevice, comprising: determining a measured dose image through anelectronic portal dose imaging device (EPID), the measured dose imageindicating a dose distribution of radiation beams measured by the EPID,and the measured radiation beams corresponding to a planned radiationdose and a planned gantry angle; determining an energy fluencedistribution map related to radiation beams predicted by a first portaldose prediction model, the predicted radiation beams corresponding tothe planned radiation dose and the planned gantry angle; determining apredicted dose image based on the energy fluence distribution map and asimulated energy response curve related to the EPID, the predicted doseimage indicating a dose distribution of the predicted radiation beams;and determining differences between the measured dose image and thepredicted dose image by comparing the dose distributions of the measureddose image and the predicted dose image.
 11. The method of claim 10,wherein the determining a measured dose image includes: obtaining aplurality of raw images with respect to the measured radiation beamsthrough the EPID; obtaining one or more calibration parameters;calibrating, based on the one or more calibration parameters, each ofthe plurality of raw images; forming a final calibrated image based onthe plurality of calibrated raw images; and converting the finalcalibrated image to the measured dose image.
 12. The method of claim 10,wherein the determining a measured dose image includes: obtaining aplurality of raw images with respect to the measured radiation beamsthrough the EPID; obtaining one or more calibration parameters;summarizing the plurality of raw images; forming a final calibratedimage by calibrating, based on the one or more calibration parameters,the summarized raw image; and converting the final calibrated image tothe measured dose image.
 13. The method of claim 12, wherein the one ormore calibration parameters include at least one of a position offsetvalue, a detector gain value, or a curve correction value.
 14. Themethod of claim 13, further comprising: determining the position offsetbased on position deviations of first measured flood-field imagesrelative to a center of the EPID; determining the detector gain valuebased on a second measured flood-field image and a beam profile value;and determining the curve correction value based on a third measuredflood-field image and a predicted flood-field image, wherein the thirdmeasured flood-field image is associated with the second measuredflood-field image, and the predicted flood-field image is generatedusing a second portal dose prediction model.
 15. The method of claim 14,wherein the first portal dose prediction model or the second portal doseprediction model includes a Monte Carlo (MC) simulation model.
 16. Themethod of claim 10, wherein the determining an energy fluencedistribution map related to radiation beams predicted by a first portaldose prediction model includes: correcting a predicted output factor ofthe first portal dose prediction model based on an output correctionfactor; and determining the energy fluence distribution map by feedingthe corrected output factor to the first portal dose prediction model.17. The method of claim 16, wherein the determining a predicted doseimage based on the energy fluence distribution map and a simulatedenergy response curve related to the EPID includes: determining anintermediate predicted dose image based on the energy fluencedistribution map and the simulated energy response curve; anddetermining the predicted dose image by correcting the intermediatepredicted dose image using an absolute dose correction factor.
 18. Themethod of claim 10, wherein the simulated energy response curve relatedto the EPID is determined in advance by modeling an energy depositionefficiency of the EPID.
 19. A non-transitory computer-readable medium,comprising at least one set of instructions, wherein when executed by atleast one processor of a computer device, the at least one set ofinstructions directs the at least one processor to perform operationsincluding: determining a measured dose image through an electronicportal dose imaging device (EPID), the measured dose image indicating adose distribution of radiation beams measured by the EPID, and themeasured radiation beams corresponding to a planned radiation dose and aplanned gantry angle; determining an energy fluence distribution maprelated to radiation beams predicted by a first portal dose predictionmodel, the predicted radiation beams corresponding to the plannedradiation dose and the planned gantry angle; determining a predicteddose image based on the energy fluence distribution map and a simulatedenergy response curve related to the EPID, the predicted dose imageindicating a dose distribution of the predicted radiation beams; anddetermining differences between the measured dose image and thepredicted dose image by comparing the dose distributions of the measureddose image and the predicted dose image.
 20. The non-transitorycomputer-readable medium of claim 19, wherein the first portal doseprediction model includes a Monte Carlo (MC) simulation model.